Sentence examples for training information systems from inspiring English sources

Human capital: individuals' know-how and commitment to fit research in; Relationship capital: It is well established that strong relationships can change behaviour; and Structural capital: the training, information systems, and other inputs required to improve readiness.

These three areas are: Human capital: individuals' know-how and commitment to fit research in; Relationship capital: It is well established that strong relationships can change behaviour; and Structural capital: the training, information systems, and other inputs required to improve readiness.

Let (mathcal {I}) be a training information system over (Sigma ).

Furthermore, the training information system (mathcal {I}) used for learning C depends on C only via its modal depth.

Setting 3 is closer to binary classification in traditional machine learning, where the interpretation (mathcal {I}) and the sets (E^+), (E^-) form a training information system.

It is well known that any Boolean function in classical propositional calculus can be learned correctly if the training information system is good enough.

That is, there exists a learning algorithm such that, for every concept C of those logics, there exists a training information system such that applying the learning algorithm to it results in a concept equivalent to C.

For each random origin concept (C), we used (E^+ = {a mid a^mathcal {I}in C^mathcal {I}}) as the set of positive examples and (E^- = {a mid a^mathcal {I}in Delta ^mathcal {I}!setminus !C^mathcal {I}}) as the set of negative examples, where (mathcal {I}) is the considered interpretation used as the training information system.

We prove that any concept in any description logic that extends (mathcal {ALC}) with some features amongst I (inverse roles), (Q_k) (qualified number restrictions with numbers bounded by a constant k), and (mathsf {Self}) (local reflexivity of a role) can be learned correctly if the training information system (specified as a finite interpretation) is good enough.

In this paper, we prove that any concept in any description logic that extends the basic DL (mathcal {ALC}) with some features amongst I (inverse roles), (Q_k) (qualified number restrictions with numbers bounded by a constant k), and (mathsf {Self}) (local reflexivity of a role) can be learned if the training information system (specified as a finite interpretation) is good enough.

For every concept C in any description logic that extends (mathcal {ALC}) with some features amongst I, (Q_k), (mathsf {Self}), there exists a training information system such that applying the MiMoD algorithm to it results in a concept equivalent to C.